Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning

Abstract

Many recent efforts in the diagnostic field address the accessibility of cancer diagnosis. Typical histological staining methods identify cancer cells visually by a larger nucleus with more condensed chromatin. Machine learning (ML) has been incorporated into image analysis for improving this process. Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormities. In this paper, a wideband electrical sensor and data analysis paradigm that can identify nuclear changes shows the realization of a single-cell microfluidic device to detect nuclei of altered sizes. To model cells of altered nucleus, Jurkat cells were treated to enlarge or shrink their nucleus followed by broadband sensing to obtain the S-parameters of single cells. The ability to deduce important frequencies associated with nucleus size is demonstrated and used to improve classification models in both binary and multiclass scenarios, despite a heterogeneous and overlapping cell population. The important frequency features match those predicted in a double-shell circuit model published in prior work, demonstrating a coherent new analytical technique for electrical data analysis. The electrical sensing platform assisted by ML with impressive accuracy of cell classification looks forward to a label-free and flexible approach to cancer diagnosis.

Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning

“…The combined use of feature selection and classification together can provide insights when sensitivity makes the separation of populations difficult. An example of this is our previous work classifying cells in populations using electrical impedance data at 201 frequencies ranging from 9 kHz to 9 GHz to identify changes in nucleus size [58]. In this work, we found that the combination of feature selection using recursive feature elimination (RFE) when combined with SVM both improved the accuracy of predictions but also could identify the most relevant frequency features.…”

Myalgic encephalomyelitis/chronic fatigue syndrome from current evidence to new diagnostic perspectives through skeletal muscle and metabolic disturbances

“…The differences have been detected using a nanoneedle bioarray 209 . In our group, model muscle cell lines chemically treated to present elevated oxidative stress 227 or intracellular calcium levels 228 have also been shown to present distinct electrical signatures compared to untreated cells. Since elevated oxidative stress and alteration in calcium signalling are typical characteristics of ME/CFS skeletal muscle tissues, the electrical characteristics of muscle cells have been proposed as a signature to define ME/CFS muscle dysfunction, while this idea has yet to be tested using clinical samples.…”

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

“…Recently, impedance spectrometers have been shown to generate all-inclusive lab-on-a-chip platforms to detect nucleus abnormalities. The paper, presented by Ferguson et al [ 117 ], is a proof-of-concept study on the classification of cancerous cells using a biosensor that employs impedance-based spectroscopy to identify the type of cells based on the size of their nucleus. The biosensor consists of a microfluidic channel attached to a quartz substrate containing an ultra-wideband waveguide.…”